2,206 research outputs found
A convolutional neural network aided physical model improvement for AC solenoid valves diagnosis
This paper focuses on the development of a physics-based diagnostic tool for alternating current (AC) solenoid valves which are categorized as critical components of many machines used in the process industry. Signal processing and machine learning based approaches have been proposed in the literature to diagnose the health state of solenoid valves. However, the approaches do not give a physical explanation of the failure modes. In this work, being capable of diagnosing failure modes while using a physically interpretable model is proposed. Feature attribution methods are applied to CNN on a large data set of the current signals acquired from accelerated life tests of several AC solenoid valves. The results reveal important regions of interest on current signals that guide the modeling of the main missing component of an existing physical model. Two model parameters, which are the shading ring and kinetic coulomb forces, are then identified using current measurements along the lifetime of valves. Consistent trends are found for both parameters allowing to diagnose the failure modes of the solenoid valves. Future work will consist of not only diagnosing the failure modes, but also of predicting the remaining useful life
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ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks.
BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster
Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules
Various faults can occur during the operation of PV arrays, and both the
dust-affected operating conditions and various diode configurations make the
faults more complicated. However, current methods for fault diagnosis based on
I-V characteristic curves only utilize partial feature information and often
rely on calibrating the field characteristic curves to standard test conditions
(STC). It is difficult to apply it in practice and to accurately identify
multiple complex faults with similarities in different blocking diodes
configurations of PV arrays under the influence of dust. Therefore, a novel
fault diagnosis method for PV arrays considering dust impact is proposed. In
the preprocessing stage, the Isc-Voc normalized Gramian angular difference
field (GADF) method is presented, which normalizes and transforms the resampled
PV array characteristic curves from the field including I-V and P-V to obtain
the transformed graphical feature matrices. Then, in the fault diagnosis stage,
the model of convolutional neural network (CNN) with convolutional block
attention modules (CBAM) is designed to extract fault differentiation
information from the transformed graphical matrices containing full feature
information and to classify faults. And different graphical feature
transformation methods are compared through simulation cases, and different
CNN-based classification methods are also analyzed. The results indicate that
the developed method for PV arrays with different blocking diodes
configurations under various operating conditions has high fault diagnosis
accuracy and reliability
Automated optical inspection of solder paste based on 2.5D visual images
In this paper, a special technique for the inspection of solder paste using directional LED lighting is presented. Conventional optical inspection method would depend on an image acquired from a camera mounted from the top. This 2D inspection of solder paste based on images is fast but is limited to defect such as bridge or no solder. Defects related to the volume of the printed solder paste or unevenness of the paste cannot be treated from a top image. The developed technique of this paper would involve the use of special directional side lighting to acquire two-and-a-half dimensional (2.5D) images from above the solder paste block. A sequence of three images is acquired and image processing is carried out for defect detection of the printed solder paste. The acquired images would highlight the geometrical features of the solder paste block. Solder paste inspection is then carried out based on the highlighted features. The proposed method can handle other types of defects that cannot be treated by conventional top light images. ©2009 IEEE.published_or_final_versio
Neural Intersection Function
The ray casting operation in the Monte Carlo ray tracing algorithm usually
adopts a bounding volume hierarchy (BVH) to accelerate the process of finding
intersections to evaluate visibility. However, its characteristics are
irregular, with divergence in memory access and branch execution, so it cannot
achieve maximum efficiency on GPUs. This paper proposes a novel Neural
Intersection Function based on a multilayer perceptron whose core operation
contains only dense matrix multiplication with predictable memory access. Our
method is the first solution integrating the neural network-based approach and
BVH-based ray tracing pipeline into one unified rendering framework. We can
evaluate the visibility and occlusion of secondary rays without traversing the
most irregular and time-consuming part of the BVH and thus accelerate ray
casting. The experiments show the proposed method can reduce the secondary ray
casting time for direct illumination by up to 35% compared to a BVH-based
implementation and still preserve the image quality
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